Lev Barinov

Speckle reduction of medical ultrasound using Compressive Re-Sampling and instantaneous SNR

Medical Ultrasonography is a valuable imaging technology for medical diagnostics and to guide interventional procedures. However, ultrasound imaging suffers from speckle noise, an inherent characteristic of all coherent imaging techniques due to the presence of sub-resolution scatterers. Speckle noise produces a reduction in contrast resolution which is responsible for the overall lower effective resolution of ultrasound compared to x-ray or MRI imaging. In the case of breast imaging, ultrasound speckle can mask small details such as low contrast tumors or microcalcifications, which may be an early indication of breast cancer. This limitation prevents ultrasound from displacing mammography as the gold standard for breast cancer screening. Traditional speckle reduction techniques attempt to remove speckle noise while preserving edges and other important features but there is always a tradeoff between removing the speckle noise and blurring tissue structure and details. We introduce a novel speckle reduction and contrast enhancement method for ultrasound imaging that is motivated by the fundamental ideas behind compressive sampling. We also introduce a way to estimate instantaneous SNR in order to identify the areas that are mostly signal from the areas that are mostly noise in order to preserve the signal while suppressing the noise. We have shown improvements in SNR on the order of 12dB in the lab and improved visualization of clinical data.

Speckle reduction using stepped-frequency continuous wave ultrasound

Medical Ultrasonography is a valuable imaging technology for medical diagnostics and to guide interventional procedures. Ultrasound imaging is particularly useful in breast cancer detection and diagnosis for women with dense breast tissue where traditional mammography may fail to detect suspicious areas. However, ultrasound imaging suffers from speckle noise, an inherent characteristic of all coherent imaging techniques due to the presence of sub-resolution scatterers. Speckle noise produces a reduction in contrast resolution which is responsible for the overall lower effective resolution of ultrasound compared to x-ray or MRI imaging. In the case of breast imaging, ultrasound speckle can mask small details such as low contrast tumors or micro-calcifications, which may be an early indication of breast cancer. This limitation prevents ultrasound from displacing mammography as the gold standard for breast cancer screening. In conventional pulsed ultrasound imaging systems, de-noising techniques are used to minimize the effect of speckle noise. However, research shows that there is a tradeoff between the effectiveness of speckle reduction techniques and image resolution. We introduce stepped-frequency continuous wave ultrasound imaging which provides a framework where speckle reduction techniques are particularly effective, resulting in higher quality images with an improved SNR and significantly lower speckle noise while maintaining the spatial resolution of the original scan so that small lesions of interest are visible to the radiologist.

An automatic identification and monitoring system for coral reef fish

To help gauge the health of coral reef ecosystems, we developed a prototype of an underwater camera module to automatically census reef fish populations. Recognition challenges include pose and lighting variations, complicated backgrounds, within-species color variations and within-family similarities among species. An open frame holds two cameras, LED lights, and two ‘background’ panels in an L-shaped configuration. High-resolution cameras send sequences of 300 synchronized image pairs at 10 fps to an on-shore PC. Approximately 200 sequences containing fish were recorded at the New York Aquarium’s Glover’s Reef exhibit. These contained eight ‘common’ species with 85–672 images, and eight ‘rare’ species with 5–27 images that were grouped into an ‘unknown/rare’ category for classification. Image pre-processing included background modeling and subtraction, and tracking of fish across frames for depth estimation, pose correction, scaling, and disambiguation of overlapping fish. Shape features were obtained from PCA analysis of perimeter points, color features from opponent color histograms, and ‘banding’ features from DCT of vertical projections. Images were classified to species using feedforward neural networks arranged in a three-level hierarchy in which errors remaining after each level are targeted by networks in the level below. Networks were trained and tested on independent image sets. Overall accuracy of species-specific identifications typically exceeded 96% across multiple training runs. A seaworthy version of our system will allow for population censuses with high temporal resolution, and therefore improved statistical power to detect trends. A network of such devices could provide an ‘early warning system’ for coral ecosystem collapse.

Preprocessing for improved computer aided detection in medical ultrasound

Recently, a new speckle noise reduction and contrast enhancement technique has been introduced that is motivated by the research in compressive sampling or sensing. Compressive sampling is based on the principle that a sparse signal such as ultrasound can be fully recovered when sampled below the Nyquist rate. This allows for a new noise reduction technique that preserves the high frequency and fine details while reducing the effects of speckle noise. This method improves the overall perceptual quality of the image for visualization and diagnosis by the radiologist. This paper examines how the improvement in SNR makes the method suitable as a preprocessor to improve a computer aided detection (CAD) system for breast cancer detection. Classical performance metrics such as false positive rates, false negative rates and receiver operator curves will be used to show the benefits of this approach. Initial experiments look promising for microcalcification detection, where the new method yields a false negative rate of 20 percent at a false positive rate of 0.5 percent while the traditional speckle reduction techniques yield a false negative rate of 60 percent at a false positive rate of 0.5 percent.

Face recognition for uncontrolled environments

A new face recognition algorithm has been proposed which is robust to variations in pose, expression, illumination and occlusions such as sunglasses. The algorithm is motivated by the Edit Distance used to determine the similarity between strings of one dimensional data such as DNA and text. The key to this approach is how to extend the concept of an Edit Distance on one-dimensional data to two-dimensional image data. The algorithm is based on mapping one image into another and using the characteristics of the mapping to determine a two-dimensional Pictorial-Edit Distance or P-Edit Distance. We show how the properties of the mapping are similar to insertion, deletion and substitution errors defined in an Edit Distance. This algorithm is particularly well suited for face recognition in uncontrolled environments such as stand-off and other surveillance applications. We will describe an entire system designed for face recognition at a distance including face detection, pose estimation, multi-sample fusion of video frames and identification. Here we describe how the algorithm is used for face recognition at a distance, present some initial results and describe future research directions.(

Automated skin thickening extraction in post radiotherapy mammograms via feedforward neural networks using histogram based segmentation and continuous hidden Markov model generated features

The purpose of this study is to establish an automated technique that accurately and effectively characterizes skin thickening in mammograms after breast conserving surgery and radiation therapy (BCS+RT).